💎 Diamond Price Prediction

An end-to-end machine learning project predicting diamond prices using the industry's 4C model (Cut, Color, Clarity, Carat) with advanced feature engineering.

What makes this project different? I benchmarked linear vs. ensemble models, engineered PCA + polynomial features, and deployed the best model (XGBoost, R² = 0.982) as a Streamlit web app.

Featured Content:

• 📊 EDA & Feature Engineering — PCA, polynomial features, encoding
• 🤖 Model Comparison — Linear, RF, Gradient Boosting, XGBoost
• 🚀 Deployment — Interactive Streamlit web app for real-time prediction
• 💡 Insights — Carat weight strongest predictor (92% correlation), Ideal cut commands premium

Key Insights:

• Carat weight dominates pricing (R² = 0.92 with price).
• Cut quality (Ideal & Premium) drives premium valuation.
• Color + Clarity add moderate but complementary effects.
• Business Strategy: Ideal cut + VS2 clarity balance brilliance and affordability.

Outcome:

Delivered a production-ready predictive model with a Streamlit app that provides real-time price estimates and business insights — supporting jewelers, sellers, and buyers in making smarter pricing and inventory decisions.

🛠️ Tools & Tech:

• 📊 Python (pandas, numpy, seaborn, matplotlib)
• 🤖 scikit-learn · XGBoost
• 🌐 Streamlit · Pickle for model storage

Explore the full Project on Github | 🌐 Live Dashboard

⚖️ SuperStore Sales Analysis & Profit Forecasting

An end-to-end financial analytics & machine learning project applying predictive and prescriptive analytics to the SuperStore dataset.

💡 What makes this project different?

I combined profit classification (85%+ accuracy), sales forecasting, and Power BI dashboards to deliver ROI-driven strategies for management.

Featured Content

• 📊 EDA & Feature Engineering — Profit margins, CLV, discount effectiveness
• 🤖 Predictive Models — Regression, classification (profitable vs. loss-making), sales forecasting
• 📈 Business Intelligence — Power BI dashboards with drill-down by region, segment, category
• 💡 Strategic Insights — Discount thresholds, customer segmentation, regional performance metrics

Key Insights

• Discounting erodes margins — aggressive discounts cut profitability
• Customer segmentation pays off — top customers increase CLV by 15–25%
• Regional performance varies — tailored strategies improve profitability by 20–30%
• Seasonal trends matter — forecasting improves inventory & cash flow

Outcome

Delivered executive-ready dashboards and a profit classification model (85%+ accuracy), enabling strategies for:

• Margin optimisation
• Customer retention
• Regional growth
• ROI-focused decision-making

🛠️ Tools & Tech

• 📊 Python (pandas, NumPy, matplotlib, seaborn, scikit-learn)
• 🤖 Regression · Classification · Forecasting
• 📈 Power BI (interactive dashboards, variance analysis, ROI scenarios)

Explore the full Project on Github | 🌐 Live Dashboard

📊 ML Zoomcamp — Applying Machine Learning in Financial Data Analysis

An end-to-end learning project following the DataTalks.Club ML Zoomcamp, designed to deepen my machine learning skills in a finance-focused context.

💡 What makes this journey different?

I'm learning in public — documenting each module with financial case studies, code samples, and real-world insights, bridging theory with practical application.

Featured Content:

• 🗒️ Regression — Predicting asset prices, loan performance, financial KPIs
• 🗓️ Classification — Customer churn, fraud detection, credit risk
• 🧮 Evaluation Metrics — Precision, recall, F1 for finance model accuracy
• ♻️ Model Deployment — Pipelines for production-ready financial models
• 🌳 Decision Trees & Ensembles — Portfolio risk & investment strategies
• 🤖 Neural Networks — Capturing complex financial patterns
• ☁️ Serverless Deep Learning — Real-time transaction analysis at scale
• 🛠️ Kubernetes — Deploying ML for high-volume financial data

Key Insights:

• Regression & Classification underpin financial forecasting and risk analysis.
• Evaluation metrics ensure models balance precision, recall, and ROI.
• Deployment pipelines are essential for production-ready finance models.
• Ensemble & neural models improve robustness for complex financial behaviour.

Outcome:

Building a comprehensive portfolio of finance-focused ML projects, including:

• Forecasting & profitability analysis
• Risk management models
• Customer churn prediction
• Real-time analytics with scalable deployments

🛠️ Tools & Tech:

• 📊 Python (pandas, NumPy, scikit-learn, matplotlib, seaborn)
• 🤖 Regression · Classification · Ensemble Learning · Neural Networks
• ☁️ Streamlit · Kubernetes · Serverless ML

🌐 Community:

This journey is powered by DataTalks.Club.

Join the Slack channel (#course-ml-zoomcamp) for collaboration and support.

💥 Let's make an impact in financial data analysis together!

Explore the full Project on Github

📈 Portfolio Stock Market Analysis of Tech Giants

An end-to-end stock market & portfolio optimization project analyzing Apple (AAPL), Microsoft (MSFT), Google (GOOGL), and Amazon (AMZN) against the S&P 500 benchmark, using return analytics, risk evaluation, and mean-variance optimization.

💡 What makes this project different?

I combined performance metrics (daily returns, volatility, Sharpe ratio) with portfolio optimization (Markowitz Efficient Frontier) to build risk-adjusted strategies investors can use for growth and downside protection.

Featured Content

• 📊 Return Analytics — mean, median, geometric & harmonic means, cumulative returns
• ⚖️ Risk & Distribution — volatility, skewness, kurtosis, coefficient of variation
• 📈 Benchmarking vs. S&P 500 — daily return plots, correlation heatmaps, Beta & Alpha regression
• 🧮 Portfolio Optimization — equal-weight portfolio vs. optimized portfolio (Sharpe-maximizing) with Efficient Frontier

Key Insights

• Apple dominates growth (512% cumulative return) but with higher volatility (std: 0.0204).
• Amazon shows high upside but heavy tail risk (kurtosis: 14.8, skew: 0.85).
• Google & Microsoft deliver steadier returns with lower risk, making them stabilizers.
• Sharpe Ratios: Apple leads (1.11), Microsoft lags (0.73) — highlighting differences in risk-adjusted performance.
• Optimized portfolio outperforms equal-weight:
• CAGR ↑ from 18.2% → 20.5%
• Sharpe Ratio ↑ from 1.20 → 1.24
• Cumulative return ↑ from 1,735% → 2,469%

Outcome

Delivered investment-ready insights and optimized portfolios that enable:

• Superior risk-adjusted growth (higher Sharpe & Sortino ratios)
• Capital allocation that balances volatility with return
• Robust downside protection during market stress
• Data-driven investment decision support

🛠️ Tools & Tech

• 📊 Python (pandas, NumPy, matplotlib, seaborn, plotly)
• 📈 yfinance · QuantStats · PyPortfolioOpt
• ⚖️ Regression · Risk Models · Efficient Frontier

Explore the full Project on Github